Introduction to AI and Machine Learning on Google Cloud

Professional AI developers, data scientists, and ML engineers who want to build predictive and generative AI projects on Google Cloud

• Recognize the data-to-AI technologies and tools provided by Google Cloud.
• Build generative AI projects by using Gemini multimodal, efficient prompts, and model tuning.
• Explore various options for developing an AI project on Google Cloud.
• Create an ML model from end-to-end by using Vertex AI.

Module 01: AI Foundations

Topics M1

• Why AI?
• AI/ML framework on Google Cloud
• Google Cloud infrastructure
• Data and AI products
• ML model categories
• BigQuery ML
• Lab introduction: BigQuery ML

Objectives M1

• Recognize the AI/ML framework on Google Cloud.
• Identify the major components of Google Cloud infrastructure.
• Define the data and ML products on Google Cloud and how they support the data-to-AI lifecycle.
• Build an ML model with BigQueryML to bring data to AI.

Activities M1

• Lab: Predicting Visitor Purchases with BigQuery ML
• Quiz
• Reading

Module 02: AI Development Options

Topics M2

• AI development options
• Pre-trained APIs
• Vertex AI
• AutoML
• Custom training
• Lab introduction: Natural Language API

Objectives M2

• Define different options to build an ML model on Google Cloud.
• Recognize the primary features and applicable situations of pre-trained APIs, AutoML, and custom training.
• Use the Natural Language API to analyze text.

Activities M2

• Lab: Entity and Sentiment Analysis with Natural Language API
• Quiz
• Reading

Module 03: AI Development Workflow

• ML workflow
• Data preparation
• Model development
• Model serving
• MLOps and workflow automation
• Lab introduction: AutoML
• How a machine learns

Objectives M3

• Define the workflow of building an ML model.
• Describe MLOps and workflow automation on Google Cloud.
• Build an ML model from end to end by using AutoML on Vertex AI.

Activities M3

• Lab: Vertex AI: Predicting Loan Risk with AutoML
• Quiz
• Reading

Module 04: Generative AI

Topics M4

• Generative AI and workflow
• Gemini multimodal
• Prompt design
• Model tuning
• Model Garden
• AI solutions
• Lab introduction: Vertex AI Studio

Objectives M4

• Define generative AI and foundation models.
• Use Gemini multimodal with Vertex AI Studio.
• Design efficient prompt and tune models with different methods.
• Recognize the AI solutions and the embedded Gen AI features.

Activities M4

• Lab: Getting Started with Vertex AI Studio
• Quiz
• Reading

Module 05: Course Summary

Topics M5

• Course summary

Objectives M5

• Recognize the primary concepts, tools, technologies, and products learned in the course.

Activities M5

• Reading

Having one or more of the following:

• Basic knowledge of machine learning concepts
• Prior experience with programming languages such as SQL and Python